Application of neural networks to detect eccentricity of induction motors

• Autorzy: Ewert P.

Identyfikatory

DOI

10.5277/PED170209

• Języki publikacji: EN

Abstrakty

EN

The possibility of using neural networks to detect eccentricity of induction motors has been presented. A field-circuit model, which was used to generate a diagnostic pattern has been discussed. The formulas describing characteristic fault frequencies for static, dynamic and mixed eccentricity, occurring in the stator current spectrum, have been presented. Teaching and testing data for neural networks based on a preliminary analysis of diagnostic signals (phase currents) have been prepared. Two types of neural networks were discussed: general regression neural network (GRNN) and multilayer perceptron (MLP) neural network. This paper presents the results obtained for each type of the neural network. Developed neural detectors are characterized by high detection effectiveness of induction motor eccentricity.

Słowa kluczowe

EN

neural network; general regression neural network; multilayer perceptron; eccentricity; induction motor

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2017
• Tom: Vol. 2 (37), No. 2
• Strony: 151--165
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Ewert P.

• Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• [1] EWERT P., WOLKIEWICZ M., Detection methods overview of induction motor eccentricity using stator current analysis, Scientific Papers of the Institute of Electrical Machines, Drives and Measurements of the Wrocław University of Technology, Studies and Research, 2015, 35, 151-160 (in Polish).
• [2] KOWALSKI C.T., ORLOWSKA-KOWALSKA T., Application of neural networks for the induction motor faults detection, Trans. of IMCAS Mathematics and Computers in Simulation, 2003, 63(3-5), 435-448.
• [3] BOUZID M.B.K., CHAMPENOIS G., BELLAAJ N.M., SIGNAC L., JELASSI K., An effective neural approach for the automatic location of stator interturn faults in induction motor, IEEE Trans. Ind. Electron., 2008, 55(12), 4277-4289.
• [4] AWADALLAH M.A., MORCOS M.M., Application of AI tools in fault diagnosis of electrical machines and drives. An overview, IEEE Trans. En. Conv., 2003, 18(2), 245-251.
• [5] NANDI S., TOLIYAT H.A., LI X., Condition monitoring and fault diagnosis of electrical motors. A review, IEEE Trans. En. Conv., 2005, 20(4), 719-729.
• [6] KOWALSKI C., KAMIŃSKI M., Rotor fault detector of the converter-fed induction motor based on RBF neural network, Bull. Polish Acad. Sci., Techn. Sci., 2014, 62(1), 69-76.
• [7] SPECHT D.F., A general regression neural network, IEEE Trans. Neural Netw., 1991, 2(6), 568-576.
• [8] CARDOSO G. Jr., ROLIM J., ZURN H.H., Application of neural network modules to electric power system fault section estimation, IEEE Trans. on Power Delivery, 2004, 19(3), 1034-1041.
• [9] FAIZ J., EBRAHIMI B.M., AKIN B., TOLIYAT H.A., Comprehensive eccentricity fault diagnosis in induction motors using finite element method, IEEE Trans. Magn., 2009, 45(3), 1764-1767.
• [10] EWERT P., KAMIŃSKI M., KOWALSKI C., Eccentricity detection of the induction motors using general regression neural networks, 10th International Conference on Modeling and Simulation of Electric Machines, Converters and System, ELECTRIMACS 2011, Paris, France, 2011, 1-6.
• [11] ILAMPARITHI T., NANDI S., Comparison of results for eccentric cage induction motor using finite element method and modified winding function approach, Joint International Conference on Power Electronics, Drives and Energy Systems (PEDES), 20-23 December 2010, 1-7.
• [12] LAZARO J., ARIAS J., MARTIN J.L., DE ALEGRIA I.M., ANDREU J., JIMENEZ J., An implementation of a general regression network on FPGA with direct Matlab link, Proc. IEEE of International Conference on Industrial Technology IEEE-ICIT 2004, 2004, 3, 1150-1155.
• [13] VAS P., Artificial intelligence-based electrical machines and drives. Applications of Fuzzy, Neural, Fuzzy-Neural and Genetic Algorithm Based Techniques, Oxford University Press, 1999.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).